Project Grant R01CA309499
- The National Cancer Institute (CFDA 93.395 - Cancer Treatment Research) awarded Nightstar Biotechnologies, Inc. a $360,442 Project Grant to develop an artificial intelligence (AI) platform called PMTNET for designing optimal therapeutic T-cell receptor (TCR-T) cells to treat cholangiocarcinoma (CCA). The PMTNET platform aims to address the challenges in traditional TCR-T cell design by predicting TCR-antigen interactions to enable targeting of minimally expressed tumor antigens while avoiding...
- This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $833,467 to the Dana-Farber Cancer Institute, Inc. (DFCI) to develop and deploy open-source AI tools to improve cancer clinical trial feasibility and recruitment. The key products and services to be delivered include: Extending DFCI's existing MatchMiner tool to match patients to clinical trials based on additional clinical variables beyond molecular criteria, such as...
- The National Cancer Institute (CFDA 93.396 - Cancer Biology Research) awarded a $285,599 Project Grant to Sinopia Biosciences Inc., a San Diego-based company specializing in computational drug discovery and metabolomics research. The grant will support the development of a metabolomics-enabled AI/ML platform for identifying new treatments to enhance drug sensitivity in cancer. The project aims to leverage high-throughput omics technologies, machine learning, and a chemical library of ~3,300...
- This $593,383 Project Grant, awarded by the National Cancer Institute under the Cancer Detection and Diagnosis Research program (CFDA 93.394), is supporting the development of an AI-augmented, multimodal, label-free nonlinear optical microscopy system for rapid and precise diagnosis of thyroid cancer and lymph node metastasis. The primary awardee, The Methodist Hospital Research Institute, is collaborating with The Johns Hopkins University on this project. The proposed system aims to eliminate...
- This federal Project Grant award of $224,582, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), supports the development of a computational framework that integrates knowledge-driven and data-driven artificial intelligence (AI) approaches to recommend effective immunotherapeutic cell designs for cancer treatment. The framework aims to enable transformative cancer immunotherapy treatment designs by leveraging...
- This National Science Foundation Project Grant of $606,649 will support the development of an engineered cyber-physical system combining advanced biological models and artificial intelligence methods to enable precision medicine for cancer treatment. Awarded under the Computer and Information Science and Engineering program, the funding will be used by Brigham and Women's Hospital and its parent organization Partners Healthcare System from October 2022 to September 2025. Specifically, the...
- The National Institute of Biomedical Imaging and Bioengineering (NIBIB) has awarded a $693,156 Project Grant (CFDA 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health) to the Dana-Farber Cancer Institute, Inc. (DFCI) to develop artificial intelligence (AI) algorithms for predicting prognosis and optimizing treatment selection for cutaneous squamous cell carcinoma (CSCC), a highly prevalent form of skin cancer. The project aims to train and validate AI...
- This $1,686,258 Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Onc.ai, Inc. aims to further develop and validate a deep learning radiomics biomarker for improved early response assessment in metastatic cancer therapy trials. The proposed approach uses deep learning models on CT scans at baseline and follow-up time points to generate a continuous "Serial CT Response Score" that can more accurately predict overall...
- This $435,827 Project Grant from the National Cancer Institute (NCI) under the CFDA 93.394 Cancer Detection and Diagnosis Research program supports research conducted by New York University (NYU) School of Medicine to develop deep learning methods for analyzing mass spectrometry imaging (MSI) data. The goal is to make MSI data more accessible to existing machine learning workflows by expanding the dimensionality of the data structure to treat each metabolite or lipid as an individual "color...
- This Project Grant award of $275,956.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of an engineered cyber-physical system that combines advanced biological models with state-of-the-art artificial intelligence methods for predictive, automated screening of anti-cancer drugs and optimizing their dosing. The goal is to realize a precision medicine paradigm that can improve health outcomes and reduce treatment...
This $300,002 Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) will support research at Baylor University to develop an innovative artificial intelligence (AI)-driven approach for accelerating the discovery of dual-targeted cancer theranostic agents. The research aims to develop bispecific small molecule inhibitory conjugates (BSMICs) targeting two critical enzymes in cancer cell fatty acid metabolism - stearoyl-CoA desaturase-1 (SCD-1) and fatty acid desaturase 2 (FADS2). The specific objectives are to: (1) create an AI algorithm integrating graph-string transformers and reinforcement learning to generate synthesizable molecules for highly targeted cancer theranostics, and (2) implement this AI system to generate novel FADS2 inhibitors and construct BSMICs for comprehensive in silico, in vitro, in vivo, and ex vivo evaluation. The research methodology combines advanced AI techniques with experimental validation to accelerate the discovery of new dual-targeted theranostic agents that can potentially address cancer therapy resistance through targeted molecular imaging and therapy. The award has an ultimate completion date of August 31, 2029.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 9/23/25 | ||
| Not listed | $150.0k | 9/4/25 |